Imputation is a popular technique for handling missing data. We consider a nonparametric approach to imputation using the kernel ridge regression technique and propose consistent variance estimation. The proposed variance estimator is based on a linearization approach which employs the entropy method to estimate the density ratio. The root-n consistency of the imputation estimator is established when a Sobolev space is utilized in the kernel ridge regression imputation, which enables us to develop the proposed variance estimator. Synthetic data experiments are presented to confirm our theory.

翻译：测算是处理缺失数据的一种常用技术。 我们考虑使用内核脊回归法进行估算的非参数性方法,并提出一致的差异估计。 拟议的差异估计基于一种线性方法,即采用对密度比率进行估计的酶法。 当在内核脊回归估算法中使用Sobolev空间时,估算估计值的根值一致性得到确定, 从而使我们能够开发拟议的差异估测法。 合成数据实验被提出来证实我们的理论。